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MAINT: batch_order_target_percent -> batch_market_order.

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The only downstream contex that was using batch_order_target_percent
already had all necessary prices, so calling batch_order_target_percent
was wasteful.
This commit is contained in:
Scott Sanderson
2017-04-10 21:24:51 -04:00
parent 281a17c592
commit 86fa28f9e8
4 changed files with 44 additions and 66 deletions
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tests/test_algorithm.py
+21 -24
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@@ -127,7 +127,6 @@ from zipline.test_algorithms import (
TestTargetAlgorithm,
TestTargetPercentAlgorithm,
TestTargetValueAlgorithm,
TestBatchTargetPercentAlgorithm,
SetLongOnlyAlgorithm,
SetAssetDateBoundsAlgorithm,
SetMaxPositionSizeAlgorithm,
@@ -963,7 +962,6 @@ def before_trading_start(context, data):
('order_percent', TestOrderPercentAlgorithm,),
('order_target_percent', TestTargetPercentAlgorithm,),
('order_target_value', TestTargetValueAlgorithm,),
('batch_order_target_percent', TestBatchTargetPercentAlgorithm,),
])
def test_order_methods(self, test_name, algo_class):
algo = algo_class(
@@ -1809,8 +1807,8 @@ def handle_data(context, data):
)
test_algo.run(self.data_portal)
def test_batch_order_target_percent_matches_multi_order(self):
weights = pd.Series([.3, .7])
def test_batch_market_order_matches_multiple_manual_orders(self):
share_counts = pd.Series([50, 100])
multi_blotter = RecordBatchBlotter(self.SIM_PARAMS_DATA_FREQUENCY)
multi_test_algo = TradingAlgorithm(
@@ -1818,7 +1816,7 @@ def handle_data(context, data):
from collections import OrderedDict
from six import iteritems
from zipline.api import sid, order_target_percent
from zipline.api import sid, order
def initialize(context):
@@ -1827,14 +1825,14 @@ def handle_data(context, data):
def handle_data(context, data):
if not context.placed:
for asset, weight in iteritems(OrderedDict(zip(
context.assets, {weights}
for asset, shares in iteritems(OrderedDict(zip(
context.assets, {share_counts}
))):
order_target_percent(asset, weight)
order(asset, shares)
context.placed = True
""").format(weights=list(weights)),
""").format(share_counts=list(share_counts)),
blotter=multi_blotter,
env=self.env,
)
@@ -1844,9 +1842,9 @@ def handle_data(context, data):
batch_blotter = RecordBatchBlotter(self.SIM_PARAMS_DATA_FREQUENCY)
batch_test_algo = TradingAlgorithm(
script=dedent("""\
from collections import OrderedDict
import pandas as pd
from zipline.api import sid, batch_order_target_percent
from zipline.api import sid, batch_market_order
def initialize(context):
@@ -1855,9 +1853,9 @@ def handle_data(context, data):
def handle_data(context, data):
if not context.placed:
orders = batch_order_target_percent(OrderedDict(zip(
context.assets, {weights}
)))
orders = batch_market_order(pd.Series(
index=context.assets, data={share_counts}
))
assert len(orders) == 2, \
"len(orders) was %s but expected 2" % len(orders)
for o in orders:
@@ -1865,7 +1863,7 @@ def handle_data(context, data):
context.placed = True
""").format(weights=list(weights)),
""").format(share_counts=list(share_counts)),
blotter=batch_blotter,
env=self.env,
)
@@ -1881,16 +1879,15 @@ def handle_data(context, data):
)
assert_equal(multi_stats, batch_stats)
def test_batch_order_target_percent_filters_null_orders(self):
weights = pd.Series([1, 0])
def test_batch_market_order_filters_null_orders(self):
share_counts = [50, 0]
batch_blotter = RecordBatchBlotter(self.SIM_PARAMS_DATA_FREQUENCY)
batch_test_algo = TradingAlgorithm(
script=dedent("""\
from collections import OrderedDict
from zipline.api import sid, batch_order_target_percent
import pandas as pd
from zipline.api import sid, batch_market_order
def initialize(context):
context.assets = [sid(0), sid(3)]
@@ -1898,9 +1895,9 @@ def handle_data(context, data):
def handle_data(context, data):
if not context.placed:
orders = batch_order_target_percent(OrderedDict(zip(
context.assets, {weights}
)))
orders = batch_market_order(pd.Series(
index=context.assets, data={share_counts}
))
assert len(orders) == 1, \
"len(orders) was %s but expected 1" % len(orders)
for o in orders:
@@ -1908,7 +1905,7 @@ def handle_data(context, data):
context.placed = True
""").format(weights=list(weights)),
""").format(share_counts=share_counts),
blotter=batch_blotter,
env=self.env,
)